The development of data networking in the 1960s, first as an academic exercise, and subsequently as a means of providing a host of new services, presents new challenges and opportunities to the well-established field of teletraffic theory. Apart from a few pioneering investigations, such as Mandelbrot (“Self-similar Error Clusters in Communication Systems and the Concept of Conditional Stationarity,” IEEE Trans. Communication Technology, pages 71-90, 1965), some of the features that distinguish data traffic from voice telephony were noticed as early as the late 1980s (see, e.g., Fowler, et al., “Local Area Network Traffic Characteristics with Implications for Broadband Network Congestion Management,” IEEE J. Select. Ar. Comm., 9(7):1139-1149, 1991; and Meier-Hellstern, et al., Traffic Models for ISDN Data Users: Office Automation Application,” in ITC-13, Copenhagen, pages 167-172, 1991, both incorporated herein by reference). In their pioneering study of Local Area Network (LAN) traffic, Leland, et al., “On the Self Similar Nature of Ethernet Traffic” (extended version), IEEE/ACM Transactions on Networking, 2(1), February 1994 (incorporated herein by reference in its entirety) presented data and argued for use of alternative models. By showing that sufficiently aggregated data traffic exhibited self-similarity over a wide range of time scales, the authors argued for the use of fractal models and, more explicitly, use of statistical processes exhibiting long-range dependence (LRD). In subsequent reports (e.g., Sherman, et al., “Proof of a Fundamental Result in Self-similar Traffic Modeling,” ACM SIGCOM, Computer Communications Review, 1998, incorporated herein by reference in its entirety), the authors contended that the underlying cause of self-similarity was effectively unrelated to the mechanisms of data transmission and was instead exclusively due to the nature of aggregated load. Unlike voice, individual streams of load in data networks followed distributions with heavy tails, the aggregation of which, it was argued, gave rise to its observed self-similarity. Cox (see, Cox, et al., Point Processes, Chapman and Hall, 1980; and Cox, “Long-range Dependence: A Review,” in Statistics: An Appraisal, pages 55-74, Iowa State University Press, 1984, both incorporated herein by reference) had given a related theoretical model for long-range dependence, namely an “M/G/Go queue” with heavy tailed service times.
Many of the succeeding investigations (for Web data, notably Crovella, et al., “Self-similarity in World Wide Web Traffic: Evidence and Possible Causes,” Proc. 1996 ACM Sigmetrics conference, May 1996; and Paxson, et al., “Wide-area Traffic: the Failure of Poisson Modeling,” IEEE/ACM Transactions on Networking, 3(3):226-244, June 1995, both incorporated herein by reference in their entirety) have followed the same general methodology. This consists of counting bytes and packets carried over identical non-overlapping time intervals and studying the general behavior of this process as the aggregation interval is increased both spatially and temporally.
Some studies of the impact of self-similarity on the performance of switching systems have argued in favor of the self-similar approach (e.g., Erramilli, et al., “Experimental Queueing Analysis with Long-range Dependent Packet Traffic,” IEEE/ACM Trans. on Networking, 4:209-223, 1996, incorporated herein by reference), while others have argued against it (e.g., Elwalid, et al., “Fundamental Bounds and Approximations for ATM Multiplexers with Applications to Video Teleconferencing,” IEEE J. Select. Ar. Comm., 13:1004-1016, 1995; and Ryu, et al., “The Importance of Long-range Dependence of VBR Video Traffic in ATM Traffic Engineering: Myths and Realities,” in Proc. ACM/SIGCOMM, pages 3-14, 1996, both incorporated by reference).
The latter studies argued that the short-term correlation in data traffic could capture much, if not all, of the performance impacts attributed to self-similarity. Followup work in this direction has concentrated on demonstration and causes of multi-fractality in low aggregate Web traffic (e.g., Feldmann, et al., “Dynamics of IP Traffic: a Study of the Role of Variability and the Impact of Control,” in Proceedings of the ACM/SIGCOMM, 1999, incorporated herein by reference in its entirety) and potential performance impacts of multi-fractality (e.g., Erramilli, et al., “Performance Impacts of Multi-scaling in Wide Area TCP/IP Traffic,” in IEEE Infocom 2000, March 2000, incorporated herein by reference in its entirety).
TCP connection arrival processes from various data sets were classified according to source application in Paxson, et al., supra. Here it was observed that protocols such as TELNET that tend to generate a single connection per “user session” are well modeled by Poisson processes, whereas applications such as HTTP and X11 which may generate multiple connections per “user session” are not.
Since the routers, hubs, gateways and other equipment used in packetized communication networks must perform reliably so as not to compromise network operation, and since traffic load simulation provides a vital way to test such equipment before it is installed in a network, still further improvement is needed with respect to systems and methods for simulating traffic loads so that the traffic they simulate more accurately represents the actual traffic the equipment would encounter under actual operating conditions.